# An automated pipeline to generate initial estimates for population Pharmacokinetic base models

**Authors:** Zhonghui Huang, Matthew Fidler, Minshi Lan, Iek Leng Cheng, Frank Kloprogge, Joseph F Standing

PMC · DOI: 10.1007/s10928-025-10000-z · Journal of Pharmacokinetics and Pharmacodynamics · 2025-11-06

## TL;DR

This paper introduces an automated pipeline to generate reliable initial estimates for population pharmacokinetic models, improving efficiency and accuracy in drug modeling.

## Contribution

The novel contribution is an automated pipeline that computes initial estimates for population pharmacokinetic models without manual input.

## Key findings

- The pipeline successfully generated initial estimates across 21 simulated and 13 real-life datasets.
- Final parameter estimates were closely aligned with true values or literature references.
- The tool performs well in both rich and sparse data scenarios.

## Abstract

Nonlinear mixed-effects models rely on adequate initial parameter estimates for efficient parameter optimization. Poor initial estimates can result in failed model convergence or termination with incorrect parameter estimates. Non-compartmental analysis (NCA) and other manual methods have typically been used to derive initial estimates for pharmacokinetic (PK) parameters. However, NCA struggles with sparse data and recent advances in automated modeling increasingly emphasize the need for initial estimates that require no user input. This study aimed to develop an integrated pipeline for the computation of initial estimates applicable to various data types and model structures. The designed pipeline incorporated a custom-designed algorithm that leveraged data-driven methods to generate initial estimates for both structural and statistical parameters in population pharmacokinetic (PopPK) base models. The pipeline’s performance was evaluated across twenty-one simulated datasets and thirteen real-life datasets. The results suggested that this pipeline performed well in all test cases. Initial estimates recommended by the pipeline resulted in final parameter estimates closely aligned with pre-set true values in simulated datasets or with literature references in the case of real-life data. This study provides an efficient and reliable tool for delivering PK initial estimates for population pharmacokinetic modeling in both rich and sparse data scenarios. An open-source R package has been created.

The online version contains supplementary material available at 10.1007/s10928-025-10000-z.

## Full-text entities

- **Chemicals:** tau (MESH:C000609666), aprindine (MESH:D001073), 2CMPT (-), cefaclor (MESH:D002433), ceftriaxone (MESH:D002443), fluorouracil (MESH:D005472), pindolol (MESH:D010869), tobramycin (MESH:D014031), diazepam (MESH:D003975)

## Full text

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## Figures

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## References

4 references — full list in the complete paper: https://tomesphere.com/paper/PMC12592298/full.md

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Source: https://tomesphere.com/paper/PMC12592298